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demand-forecasting

Predict inventory needs from sales history, seasonal trends, and reorder points.

3|1|Updated Mar 21, 2026
Search Tags:#Shopify#seasonality#demand forecasting#safety stock#replenishment#inventory planning#reorder point
One-click install
npx skills add https://github.com/tomtoto757/ecomm-ai-team --skill demand-forecasting-tomtoto757
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: demand-forecasting
Source: https://github.com/tomtoto757/ecomm-ai-team/tree/main/skills/analytics-reporting/finsilabs/business-operations/demand-forecasting
Command: npx skills add https://github.com/tomtoto757/ecomm-ai-team --skill demand-forecasting-tomtoto757

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Merchants relying on intuition for reorder decisions face chronic stockouts during peaks and costly overstock on slow movers. This Skill replaces guesswork with data-driven demand forecasting using historical sales, seasonality, and supplier lead times. ## Core Features & Use Cases - Platform-Specific Tooling Guidance: Step-by-step setup for Inventory Planner, ATUM, and native analytics on Shopify, WooCommerce, and BigCommerce. - Reorder Point & Safety Stock Calculation: Computes reorder points using average daily demand, lead times, and safety stock buffers, with TypeScript reference implementations for custom/headless stacks. - Seasonal Planning: Builds seasonal indices from multi-year sales history and applies promotional overrides for events like Black Friday. - Use Case: A retailer with 200 SKUs uses the replenishment logic to generate a prioritized report of critical and warning items, automatically subtracting open purchase orders to prevent double-ordering. ## Quick Start Ask the AI to calculate reorder points and generate a replenishment report for your products using the last 30 days of sales data and your supplier lead times.

Frequently Asked Questions about demand-forecasting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate a reorder point for inventory?▼

Reorder point equals average daily demand multiplied by supplier lead time in days, plus safety stock. Safety stock is computed as Z-score times residual standard deviation times the square root of lead time, using Z = 1.645 for a 95% service level.

What inventory forecasting tool works with Shopify?▼

Inventory Planner connects directly to Shopify, analyzes 12+ months of sales history, calculates reorder points, and generates purchase orders. Cogsy is another option; Shopify's built-in Analytics reports cover basic trends without an app.

How much sales history do I need for demand forecasting?▼

At least 6 months of history is needed for basic seasonal pattern detection, and 12+ months for year-over-year trends. New products under 90 days should use a category-average demand rate as a fallback proxy.

How do I forecast demand for new products with no sales history?▼

Use the category-average daily demand rate as a proxy for the first 90 days, and fall back to a category-level seasonal index when the product lacks enough history for its own. Inventory Planner has a dedicated new product mode for this.

Why does my forecast keep causing stockouts?▼

Common causes are safety stock set too low, promotional spikes inflating baseline demand, or ignoring pending purchase orders. Raise the service level to 95-98% for high-velocity SKUs and track MAPE monthly, recalibrating when error exceeds 30%.

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